Abstract

Effective health diagnosis provides multifarious
benefits such as improved safety, reliability and economical
maintenance of complex engineered systems. This paper
presents a novel multi-sensor health diagnosis method
using Deep Belief Network (DBN) based state
classification. The DBN employs a hierarchical structure
with multiple stacked Restricted Boltzmann Machines and
works through a layer by layer successive learning process.
The proposed multi-sensor health diagnosis can be
structured in three consecutive stages: first, defining health
states and collecting sensory data for DBN training and
testing; second, developing DBN based classification
models for the diagnosis of predefined health states; third,
validating DBN classification models with testing sensory
dataset. The performance of proposed DBN health state
classification is compared with four other existing
classification methods and demonstrated with a case study.

Description

Third Place winner of oral presentations at the 7th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Marcus Welcome Center, Wichita State University, May 4, 2011.

Research completed at the Department of Industrial and Manufacturing Engineering